…[4.16] and so will be greater than it would have been in the absence of measurement error, reducing the precision of the estimator. The standard errors remain valid but will be larger than they would have been in the absence of the measurement error, reflecting the loss of precision. 4.1.4.0 SUMMARY In this unit, in other for the students to have understanding of the topic stochastic regressors and measurement errors, we explained conditions under which OLS estimator remain unbiased when the variable in a regression model possessing random components. A demonstration of the unbiasedness and consistency properties was also approached. Equally, the consequences of measurement errors, errors in descriptive and dependents variables were discussed.